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Models that generate extractive rationales (i.e., subsets of features) or natural language explanations (NLEs) for their predictions are important for explainable AI.
A coefficient of agreement for nominal scales
Cohen, J · 1960
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BLEU: A method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics
Lin, C. and Och, F. J · 2004
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METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Banerjee, S. and Lavie, A · 2005
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Modeling annotators: A generative approach to learning from annotator rationales
Zaidan, O. and Eisner, J · 2008
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D · 2015
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CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., and Parikh, D · 2015
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SPICE: Semantic propositional image caption evaluation
Anderson, P., Fernando, B., Johnson, M., and Gould, S · 2016
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Generating visual explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., and Darrell, T · 2016
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Rationalizing neural predictions
Lei, T., Barzilay, R., and Jaakkola, T. S · 2016
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“Why should I trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P · 2017
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Fixing weight decay regularization in adam
Loshchilov, I. and Hutter, F · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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ConceptNet 5.5: An open multilingual graph of general knowledge
Speer, R., Chin, J., and Havasi, C · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
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Commonsense for generative multi-hop question answering tasks
Bauer, L., Wang, Y., and Bansal, M · 2018
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e-SNLI: Natural language inference with natural language explanations
Camburu, O., Rocktäschel, T., Lukasiewicz, T., and Blunsom, P · 2018
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Textual explanations for self-driving vehicles
Kim, J., Rohrbach, A., Darrell, T., Canny, J. F., and Akata, Z · 2018
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Multimodal explanations: Justifying decisions and pointing to the evidence
Park, D. H., Hendricks, L. A., Akata, Z., Rohrbach, A., Schiele, B., Darrell, T., and Rohrbach, M · 2018
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Interpretable neural predictions with differentiable binary variables
Bastings, J., Aziz, W., and Titov, I · 2019
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COMET: Commonsense transformers for automatic knowledge graph construction
Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., and Choi, Y · 2019
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Can I trust the explainer? Verifying post-hoc explanatory methods
Camburu, O.-M., Giunchiglia, E., Foerster, J., Lukasiewicz, T., and Blunsom, P · 2019
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The dangers of post-hoc interpretability: Unjustified counterfactual explanations
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., and Detyniecki, M · 2019
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Improving neural story generation by targeted common sense grounding
Mao, H. H., Majumder, B. P., McAuley, J. J., and Cottrell, G. W · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
McCoy, T., Pavlick, E., and Linzen, T · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
NILE: Natural language inference with faithful natural language explanations
Kumar, S. and Talukdar, P. P · 2020
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ALBERT: A lite BERT for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R · 2020
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Like hiking? You probably enjoy nature: Persona-grounded dialog with commonsense expansions
Majumder, B. P., Jhamtani, H., Berg-Kirkpatrick, T., and McAuley, J. J · 2020
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Natural language rationales with full-stack visual reasoning: From pixels to semantic frames to commonsense graphs
Marasovic, A., Bhagavatula, C., Park, J. S., Bras, R. L., Smith, N. A., and Choi, Y · 2020
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WT5?! Training text-to-text models to explain their predictions
Narang, S., Raffel, C., Lee, K., Roberts, A., Fiedel, N., and Malkan, K · 2020
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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
Cited alongside, same era.
Explain yourself! Leveraging language models for commonsense reasoning
Rajani, N. F., McCann, B., Xiong, C., and Socher, R · 2019
Cited alongside, same era.
ATOMIC: An atlas of machine commonsense for if-then reasoning
Sap, M., Bras, R. L., Allaway, E., Bhagavatula, C., Lourie, N., Rashkin, H., Roof, B., Smith, N. A., and Choi, Y · 2019
Cited alongside, same era.
Do human rationales improve machine explanations?
Strout, J., Zhang, Y., and Mooney, R. J · 2019
Cited alongside, same era.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., and Berant, J · 2019
Cited alongside, same era.
Does it make sense? And why? A pilot study for sense making and explanation
Wang, C., Liang, S., Zhang, Y., Li, X., and Gao, T · 2019
Cited alongside, same era.
Faithful multimodal explanation for visual question answering
Wu, J. and Mooney, R. J · 2019
Cited alongside, same era.
VisualCOMET: Reasoning about the dynamic context of a still image
Park, J. S., Bhagavatula, C., Mottaghi, R., Farhadi, A., and Choi, Y · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Schramowski, P., Stammer, W., Teso, S., Brugger, A., Shao, X., Luigs, H., Mahlein, A., and Kersting, K · 2020
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BLEURT: Learning robust metrics for text generation
Sellam, T., Das, D., and Parikh, A. P · 2020
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Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods
Slack, D., Hilgard, S., Jia, E., Singh, S., and Lakkaraju, H · 2020
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SemEval-2020 Task 4: Commonsense validation and explanation
Wang, C., Liang, S., Jin, Y., Wang, Y., Zhu, X., and Zhang, Y · 2020
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ERNIE-ViL: Knowledge enhanced vision-language representations through scene graph
Yu, F., Tang, J., Yin, W., Sun, Y., Tian, H., Wu, H., and Wang, H · 2020
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Learning to rationalize for nonmonotonic reasoning with distant supervision
Brahman, F., Shwartz, V., Rudinger, R., and Choi, Y · 2021
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The struggles of feature-based explanations: Shapley values vs. minimal sufficient subsets
Camburu, O.-M., Giunchiglia, E., Foerster, J., Lukasiewicz, T., and Blunsom, P · 2021
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Grounding ’grounding’ in NLP
Chandu, K. R., Bisk, Y., and Black, A. W · 2021
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Aligning faithful interpretations with their social attribution
Jacovi, A. and Goldberg, Y · 2021
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e-ViL: A dataset and benchmark for natural language explanations in vision-language tasks
Kayser, M., Camburu, O., Salewski, L., Emde, C., Do, V., Akata, Z., and Lukasiewicz, T · 2021
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Learning from the best: Rationalizing predictions by adversarial information calibration
Sha, L., Camburu, O., and Lukasiewicz, T · 2021
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Entailment as few-shot learner
Wang, S., Fang, H., Khabsa, M., Mao, H., and Ma, H · 2021
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Measuring association between labels and free-text rationales
Wiegreffe, S., Marasovic, A., and Smith, N. A · 2021
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Internet-augmented language models through few-shot prompting for open-domain question answering
Lazaridou, A., Gribovskaya, E., Stokowiec, W., and Grigorev, N · 2022
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Natural language inference with a human touch: Using human explanations to guide model attention
Stacey, J., Belinkov, Y., and Rei, M · 2022
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BERTScore: Evaluating text generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., and Artzi, Y · 2080
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